Datasets for novel view synthesis. Several datasets were proposed to support the task of novel view synthesis. NeRF [1]} introduced 8 synthetic scenes with 360-degree views. The rapid progress that followed this work was attributed to algorithmic developments rather than scaling up the data. However, for the successful application of deep learning, a large amount of data is necessary for improved generalization. Wang [2]} introduced a synthetic dataset including Google Scanned Objects. Other datasets for training multi-view algorithms include DTU [3]}, LLFF [4]}, Tanks and Temples [5]}, Spaces [6]}, RealEstate10K [7]}, SRN [8]}, Transparent Objects [9]}, ROBI [10]}, CO3D [11]}, SAPIEN [12]}, and BlendedMVS [13]}. Recently, RTMV [14]} introduced a ray-traced posed multi-view dataset with 2000 scenes and high-resolution images. Unfortunately, the current posed multi-view datasets commonly used in NeRF research are either small in image resolution or the number of posed images (SRN [8]} and NMR [16]}), small in the number of scenes/shapes and classes (Synthetic NeRFs [1]}), or lack structure (DTU [3]} and RTMV [14]}). A detailed multi-attribute comparison is provided in Table REF .
